Chris Sampson’s journal round-up for 28th March 2016

Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.

In the early 2000s, a number of states in the USA expanded Medicaid while others did not. These expansions covered similar populations to those that are likely to benefit from the Affordable Care Act. This study combines county-level statistics on unemployment, wages and enrollment in welfare programmes and uses a difference-in-differences regression to look at the effects of the expansion. The study is based on the assumption that some counties would be more affected than others, and that these groups can be identified based on the level of poverty before the expansion. Counties above the 75th percentile of poverty rates are considered the ‘treatment group’. The poorer counties saw a decrease in labour force participation rate, a decrease in working hours, an increase in wages and an increase in the use of food stamps. The main identification strategy isn’t very convincing, but the author argues that the findings are robust to alternatives. If we believe it, then the Affordable Care Act might have some modest negative consequences for employment and welfare enrollment.

Modelling is built on assumptions. Many of these will be about how we extrapolate outcomes beyond what is observed in a trial. This study reviews the ways in which cost-effectiveness studies have extrapolated survival data using external information to inform the extrapolation. Such external information might come from national statistics such as life-tables, or from large cohort studies. The authors started with NIHR HTA reports and adopted a ‘pearl growing’ approach to identifying relevant studies. Based on their findings, the authors present a framework for the assumption process. This can be followed to determine which kind of approach to extrapolation should be adopted; for example, depending on whether the control group is assumed to have the same mortality as the external population or whether this differs in the short term. The authors describe the approach that should be taken in each circumstance. It’s also important to think about uncertainty. In particular, there is likely to be uncertainty regarding the effectiveness of the treatment. No studies were identified that formally used external data to quantify future changes in treatment effects. The authors discuss the potential for the use of expert elicitation to inform survival extrapolation using Bayesian inference. If you’re building a model that requires survival data from a trial to be extrapolated, you’ll find this review to be very helpful.

There’s plenty of evidence showing that being loved by one’s parents is crucial to development. A new study of 451 adolescents followed into adulthood supports this. 12 year olds and their families were recruited for a study in Iowa, and data were collected at multiple time points up to age 20. Harsh parenting was identified by coders who watched videotapes of parent behaviour. Harsh parenting behaviours were hostility, angry coercion, physical attacks and antisocial behaviour. Adolescents’ self-assessed health and BMI were recorded throughout the study, as was their own judgment of parental warmth for each parent. The authors use a latent change score model to investigate associations between these variables. Harsh parenting was associated with a negative impact on future self-reported health and BMI. In terms of self-reported health, a positive relationship with the father helped mitigate the health impact of having a harsh mother. But then the effect on BMI seemed to increase with warmth from the other parent. The evidence suggests that as well as preventing harsh parenting, it may be worthwhile focussing on a child’s relationship with the less harsh parent as a means of buffering against the negative effects.

This study reports on the EQ-5D data from a clinical trial of cinacalcet for secondary hyperparathyroidism. Using the normal approach of estimating QALYs based on the area under the curve, no difference was identified between the two arms of the trial. But then trials like this aren’t designed to identify differences in health-related quality of life. This study explores an alternative approach. EQ-5D-3L was collected at baseline and 6 follow-up points from 3547 subjects. It was additionally collected after particular clinical events. A regression model using a generalised estimating equation (GEE) approach was fitted with EQ-5D index scores as the dependent variable and clinical events as explanatory variables along with baseline utility and trial allocation. The analysis looked at acute effects (on utility within 13 weeks) and chronic effects (on utility in all subsequent months). A regression analysis with just trial allocation as an explanatory variable only found a non-significant treatment effect. However, the GEE regression that controlled for the acute and chronic effects of clinical events was able to identify a small but significant beneficial effect of the treatment. The effect could be observed independent of the effect of clinical events. Whether such results will be as convincing as traditional trial comparisons will remain to be seen, but adopting an approach of this sort could be far more informative when determining parameters for a decision model.